24 papers · ranked by Valyu relevance
Stritzel, Oliver, Hühnerbein, Nick + 12 more
In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the future behavior of unfolding business processes and predicting Key Performance Indicators (KPIs). However, many PPM approaches often lack reproducibility, transparency in decision…
Carlos M. Gómez, Antonio Arjona, Francisco J. Ruíz-Martínez, Manuel Muñoz-Caracuel + 2 more
Predictive coding is a theory that tries to account for how the brain processes in an anticipatory manner the expected stimuli, and reorganizes the underlying neural networks as a consequence of the outcome of predictions: Correct or incorrect. EEG has the advantage of making a continuous and almost instantaneous…
Mohammad Salehi, Raouf Khayami, Reza Akbari, Mirpouya Mirmozaffari + 1 more
Process Mining (PM) effectively diagnoses inefficiencies in complex healthcare workflows, such as chemotherapy protocols. However, current methodologies often remain retrospective or rely on loosely coupled simulations, leaving a critical methodological void: the inability to quantify the aggregate, system-wide…
Martina Pasqualetti, Jakob C. B. Schwenk, Andrea Alamia
Several studies suggest that neural oscillations play a role in cognition, including predictive processing. Recent models propose that alpha-band traveling waves reflect prediction (top-down, frontal to occipital), while bottom-up waves reflect prediction errors. We tested this hypothesis using a visual statistical…
Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Samuele Latorre
This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, temporal reconstruction, event log construction, prefix-based representations, and predictive modeling to support continuous reasoning on partially observed patient…
Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn
Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event. While PPM research in recent years has been dominated by deep sequence models trained from…
Flóra Hann, Cintia Anna Nagy, Zita Olivia Nagy, Dezso Nemeth + 1 more
The ability to build predictive models of the environment fundamentally drives adaptive behavior. Yet, the real-time dynamics of how these internal models are formed and updated remain poorly understood. Conventional methods often rely on indirect, offline measures or noisy motor responses, limiting insight into the…
Alessandro Padella, Massimiliano de Leoni, Marcelo Fantinato
Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive Process Monitoring framework, which was initially focused on total time prediction…
Wen Tong, Xiaojiao Li, Yingdi Liu, Zhifang Liu + 1 more
This study employed the Ex-Gaussian distribution model to analyse eye-tracking data, to elucidate the cognitive mechanisms underlying predictive processing during Chinese reading. Using a single-factor, two-level within-subjects design (contextual predictability: high vs. low), data from 32 adult readers were analysed…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Yidan Ma, Yue Wu, Xinsheng Fang
Due to the complexity of hotel operation processes, abnormal situations are inevitable, making proactive anomaly prediction essential for ensuring operational stability. Although current deep learning methods can encode control and data flows to predict anomalies in attributes like activity and time, they often fail to…
Matthias Stierle, Karsten Kraume, Martin Matzner
Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-driven process analysis. As a consequence, awareness for the many facets of process analysis is decreasing. In particular, while an increasing focus is put onto…
Niloufar Razmi, Xufeng Caesar Dai, Leah Bakst, Matthew R. Nassar
People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers…
Koteeswaran Seerangan, Premalatha Gunasekaran, Nithya Rekha Sivakumar, Resmi Ravi Nair + 4 more
Background/Objectives: Diabetes is one of the most familiar and common diseases among people currently, and is a type of metabolic disease that is caused due to high levels of sugar in the blood for longer periods of time. If the disease is predicted at an earlier stage, the severity and risks associated with diabetes…
Wei Yang, Jinyan Liang, Xiaoyu Zhang, Xiting Peng
In the context of smart manufacturing, improving the quality and efficiency of process planning, especially in the processing of complex parts, has become a key factor influencing the level of intelligence in manufacturing systems. However, most current process planning methods still heavily rely on manual expertise…
S. Patel, V. Patel
Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
Felix Saretzky, Andersen, Lucas, Thomas Engel + 1 more
The transition to prescriptive maintenance in manufacturing is critically constrained by a dependence on predictive models. These models tend to rely on spurious correlations rather than identifying the true causal drivers of failures, often leading to costly misdiagnoses and ineffective interventions. This fundamental…
Sandeep Jain, Pradyumn Kumar Arya, Youqiang Xing
In advanced manufacturing processes, precise deposition behavior prediction is crucial for process parameter optimization. In order to forecast significant deposition responses such as bead width (w), bead height (h), energy input (EI), and volumetric input (VI) based on process parameters like laser power (P), travel…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…